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regression

Papers

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Showing 9151–9200 of 9424 papers

TitleStatusHype
Efficient Multiscale Gaussian Process Regression using Hierarchical Clustering—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Efficient Optimization Algorithms for Linear Adversarial Training—0
Efficient Optimization for Sparse Gaussian Process Regression—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Efficient Path Algorithms for Clustered Lasso and OSCAR—0
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation—0
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension—0
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data—0
Efficient Regularized Piecewise-Linear Regression Trees—0
Efficient Regularized Regression for Variable Selection with L0 Penalty—0
Efficient sampling for Gaussian linear regression with arbitrary priors—0
Efficient Sampling for Gaussian Process Inference using Control Variables—0
Efficient semi-supervised inference for logistic regression under case-control studies—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Efficient Strongly Polynomial Algorithms for Quantile Regression—0
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation—0
Efficient Symmetric Norm Regression via Linear Sketching—0
Efficient Tensor Kernel methods for sparse regression—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party—0
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets—0
Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection—0
Electoral David vs Goliath: How does the Spatial Concentration of Electors affect District-based Elections?—0
Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine—0
Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression—0
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression—0
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis—0
Electrophysiological indicators of gesture perception—0
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression—0
_1-regression with Heavy-tailed Distributions—0
_1-regression with Heavy-tailed Distributions—0
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering—0
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion—0
Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation—0
_p-Regression in the Arbitrary Partition Model of Communication—0
ELM Ridge Regression Boosting—0
Embedded Multi-label Feature Selection via Orthogonal Regression—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Embeddings for Tabular Data: A Survey—0
Embryonic Elongation controlled by Graded Cell Fate: A Molecular Dynamics Approach—0
EM Converges for a Mixture of Many Linear Regressions—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
EM for Mixture of Linear Regression with Clustered Data—0
EMLight: Lighting Estimation via Spherical Distribution Approximation—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM—0
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All datasetsCalifornia Housing PricesConcrete Compressive StrengthMedical Cost Personal DatasetSynthetic: y = x * sin xADORECar_Price_Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.79—Unverified
2Baseline RegressionR2 Score0.58—Unverified
3Mimic / SurrogateR2 Score0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.86—Unverified
2Baseline RegressionR2 Score0.59—Unverified
3Mimic / SurrogateR2 Score0.58—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.87—Unverified
2Baseline RegressionR2 Score0.78—Unverified
3Mimic / SurrogateR2 Score0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.98—Unverified
2Baseline RegressionR2 Score-0.01—Unverified
3Mimic / SurrogateR2 Score-0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoost-MACCSmicro-averaged RMSE0.93—Unverified
2RF-ToxPrintchemical macro-average RMSE0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1Linear and Decision Tree RegressionR Squared86.84—Unverified